The present disclosure relates to an image recognition system, and particularly relates to efficient image recognition processing.
Image recognition technologies for detecting objects such as humans and non-humans have been widely used in various devices such as monitoring cameras, vehicle safety devices, and digital still cameras. Such technologies are expected to be widely applied to, e.g., identification of a suspicious person, collection of marketing information, or risk prediction, by tracking an object and determining the type of the object's behavior.
Image recognition processing using an optical flow has been known as one of technologies for tracking an object (see, e.g., U.S. Pat. No. 8,374,393). Further, a technology for determining the orientation of a human's face using a software discriminator has been disclosed as one of image recognition processing technologies (in, e.g., U.S. Pat. No. 7,957,567). Moreover, as another image recognition processing technology using software, a technology for estimating the posture of a human based on feature quantity extraction focused on the edge of a human body has been disclosed (by, e.g., Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester and Deva Ramanan, “Object Detection with Discriminatively Trained Part-Based Models,” Pattern Analysis and Machine Intelligence, IEEE Transactions, Vol. 32, No. 9, pp. 1627-1645, September 2010, and Yi Yang and Deva Ramanan, “Articulated pose estimation with flexible mixtures-of-parts,” Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference, pp. 1385-1392, 20-25 Jun. 2011).
On the other hand, a reconfigurable processor implementing the image recognition processing by hardware has been known (see, e.g., Atsushi Atarashi and four other authors, “Low Power Application-Oriented Processor—taking image recognition processor IMAPCAR2 as example—,” NEC Technical Journal, Vol. 62, No. 3/2009, pp. 97-101). Japanese Unexamined Patent Publications Nos. 2001-236496 and 2007-141132 also disclose reconfigurable processors. Further, since the image recognition processing requires various types of processing steps, a processor capable of executing multiple different types of computations efficiently has been known (see, e.g., Japanese Unexamined Patent Publication No. 2010-134713).
In addition, according to some technologies, such as gesture recognition processing using a Kinect sensor developed by Microsoft®, 3D data is generated by a sensor and the output of the sensor is subjected to software processing.
There are various types of image recognition processing related technologies as described above, and such technologies advance so fast. Thus, in order to make the system flexibly adaptable to the latest technology, it can be said that it is a practical choice to implement the image recognition processing by software.